Eluding oblivion with smart stochastic selection of synaptic updates.

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Short-term plasticity as cause–effect hypothesis testing in distal reward learning (2014, background): “In general, continuous learning, or synapses that are always plastic, pose a treat to previously acquired memory (Senn and Fusi, 2005; Fusi and Senn, 2006; Leibold and Kempter, 2008).”
Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics (2007, background): “When a single output neuron is considered, arbitrary linearly separable patterns can be learned without errors (Senn & Fusi, 2005a, 2005b; Fusi & Senn, 2006) also in the extreme case of binary synapses.”